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arXiv:2601.20069v1 Announce Type: new Abstract: Training a single network with multiple objectives often leads to conflicting gradients that degrade shared representations, forcing them into a...
arXiv:2601.20071v1 Announce Type: new Abstract: Gradient-regularized value learning methods improve sample efficiency by leveraging learned models of transition dynamics and rewards to estimate return gradients....
arXiv:2601.20072v1 Announce Type: new Abstract: We address the challenge of training Vision Transformers (ViTs) when labeled data is scarce but unlabeled data is abundant. We...
arXiv:2601.20075v1 Announce Type: new Abstract: Contrastive Language-Image Pre-training (CLIP) has become a cornerstone in vision-language representation learning, powering diverse downstream tasks and serving as the...
arXiv:2601.20079v1 Announce Type: new Abstract: Heat-pipe microreactors (HPMRs) are compact and transportable nuclear power systems exhibiting inherent safety, well-suited for deployment in remote regions where...
arXiv:2601.20080v1 Announce Type: new Abstract: Finding a particular object in a display is important for viewers in many visualizations, for example, when reacting to brushing...
arXiv:2601.20084v1 Announce Type: new Abstract: Interpretability in black-box dense retrievers remains a central challenge in Retrieval-Augmented Generation (RAG). Understanding how queries and documents semantically interact...
arXiv:2601.20085v1 Announce Type: new Abstract: Programming instructors have diverse philosophies about integrating generative AI into their classes. Some encourage students to use AI, while others...
arXiv:2601.20086v1 Announce Type: new Abstract: A core assumption of Explainable AI (XAI) is that explanations are useful to users -- that is, users will do...
arXiv:2601.20090v1 Announce Type: new Abstract: Large language model (LLM)-powered agents can translate high-level user intents into plans and actions in an environment. Yet after observing...
arXiv:2601.20099v1 Announce Type: new Abstract: Humans and large language models (LLMs) now co-produce and co-consume the web's shared knowledge archives. Such human-AI collective knowledge ecosystems...
arXiv:2601.20100v1 Announce Type: new Abstract: Generative AI chatbots have proven surprisingly effective at persuading people to change their beliefs and attitudes in lab settings. However,...
arXiv:2601.20102v1 Announce Type: new Abstract: Engineering sustainable and equitable healthcare requires medical language models that do not change clinically correct diagnoses when presented with non-decisive...
arXiv:2601.20103v1 Announce Type: new Abstract: Recent advances in reinforcement learning for code generation have made robust environments essential to prevent reward hacking. As LLMs increasingly...
arXiv:2601.20104v1 Announce Type: new Abstract: Nuclei instance segmentation in hematoxylin and eosin (H&E)-stained images plays an important role in automated histological image analysis, with various...
arXiv:2601.20105v1 Announce Type: new Abstract: Figurative language, particularly fixed figurative expressions (FFEs) such as idioms and proverbs, poses persistent challenges for large language models (LLMs)....
arXiv:2601.20106v1 Announce Type: new Abstract: Autonomous AI agents are transforming software development and redefining how developers collaborate with AI. Prior research shows that the adoption...
arXiv:2601.20107v1 Announce Type: new Abstract: Recent Vision-Language Models (e.g., ColPali) enable fine-grained Visual Document Retrieval (VDR) but incur prohibitive index vector size overheads. Training-free pruning...
arXiv:2601.20109v1 Announce Type: new Abstract: The increasing adoption of AI coding agents has increased the number of agent-generated pull requests (PRs) merged with little or...
arXiv:2601.20112v1 Announce Type: new Abstract: The rise of large language models (LLMs) has accelerated the development of automated techniques and tools for supporting various software...
arXiv:2601.20113v1 Announce Type: new Abstract: The growing volume of scientific simulation data presents a significant challenge for storage and transfer. Error-bounded lossy compression has emerged...
arXiv:2601.20115v1 Announce Type: new Abstract: Graphics Processing Units (GPUs) are the state-of-the-art architecture for essential tasks, ranging from rendering 2D/3D graphics to accelerating workloads in...
arXiv:2601.20116v1 Announce Type: new Abstract: Transformer models have achieved remarkable empirical successes, largely due to their in-context learning capabilities. Inspired by this, we explore training...
arXiv:2601.20118v1 Announce Type: new Abstract: To advance Polar code design for 6G applications, we develop a reinforcement learning-based universal sequence design framework that is extensible...